Deliverable 6.5: Validity of sustainability assessment report (Part A: RISE, Part B: LCA)

Summary

Part A (RISE):
The Response Inducing Sustainability Evaluation (RISE) tool was designed to give input to farm management after data evaluation. Validity of the RISE tool was tested by changing the input data used for the computation of dedicated indicators and calculating the change in scores for these indicators and the themes they belong to. This was done by using the RISE tool and designing 9 scenarios for 5 case farms (all dairy farms).
Changing the farm data input for some parameters used in the computation of the indicators by as much as 10%, changed the score of these indicators in a contrasting manner. For four of the indicators, the scores were lower than 10%, and for two other indicators the scores were higher than 10%. These differences make it difficult to rely on the scores to check if the farm management improvements have increased the sustainability of the farm.
Part B (LCA):
A revision of the main causes of uncertainty in the Life Cycle Assessments (LCA), conducted in the frame of Organic-PLUS, has been performed. Due to the ambition of the LCA studies, and the fact that the whole production chain is included, and a high number of impact categories are reported, a large amount of data is needed. Especially for organic production systems, this data was not always available or even possible to collect.
This Part B section covers how we have addressed this lack of information. First, the introduction and methodological sections present how variability and uncertainty is addressed in LCA studies. Second, some examples from the different case studies are provided to underline potential sources of uncertainty and how to deal with it.
Between the different sources of uncertainty encountered we highlight those in relation to: i) estimating emissions instead of measuring; ii) Tier level used and iii) allocation criteria. To deal with them we have used sensitivity assessments and Monte Carlo uncertainty assessment.
Inclusion of uncertainty values is the first stage in conducting corresponding uncertainty assessments, where we can differentiate between basic uncertainty typical of the process’ variability, and that in relation to data quality of datasets used to prepare inventories.
As a result of the assessment conducted, we could conclude that major efforts are needed to improve quality of datasets to be used and improvement on methods on uncertainty values for inputs and outputs in organic production systems. Advice for further research is included.